event-store-design
Helps your agent design and implement databases for systems that record all changes as events. It's essential for building data-tracking infrastructure.
What it does
This tool helps design and set up "event stores," which are special databases for keeping records of events in software systems. It's useful for those building the foundation of a system that logs all changes as a sequence of events.
When to use it
- ✓When building the foundation of a system that records every action.
- ✓When choosing which technology to use for storing these events.
- ✓When configuring how events will be stored and retrieved.
Example of what to ask your AI
“How can I set up an event store using PostgreSQL to log user events?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool analyzes your goals and constraints, applies best practices for event store design, and provides actionable steps. It can show examples of database setup or code for managing events.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill event-store-designOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/event-store-design/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Event Store Design
Comprehensive guide to designing event stores for event-sourced applications.
Do not use this skill when
- The task is unrelated to event store design
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Use this skill when
- Designing event sourcing infrastructure
- Choosing between event store technologies
- Implementing custom event stores
- Optimizing event storage and retrieval
- Setting up event store schemas
- Planning for event store scaling
Core Concepts
1. Event Store Architecture
┌─────────────────────────────────────────────────────┐
│ Event Store │
├─────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Stream 1 │ │ Stream 2 │ │ Stream 3 │ │
│ │ (Aggregate) │ │ (Aggregate) │ │ (Aggregate) │ │
│ ├─────────────┤ ├─────────────┤ ├─────────────┤ │
│ │ Event 1 │ │ Event 1 │ │ Event 1 │ │
│ │ Event 2 │ │ Event 2 │ │ Event 2 │ │
│ │ Event 3 │ │ ... │ │ Event 3 │ │
│ │ ... │ │ │ │ Event 4 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
├─────────────────────────────────────────────────────┤
│ Global Position: 1 → 2 → 3 → 4 → 5 → 6 → ... │
└─────────────────────────────────────────────────────┘
2. Event Store Requirements
| Requirement | Description |
|---|---|
| Append-only | Events are immutable, only appends |
| Ordered | Per-stream and global ordering |
| Versioned | Optimistic concurrency control |
| Subscriptions | Real-time event notifications |
| Idempotent | Handle duplicate writes safely |
Technology Comparison
| Technology | Best For | Limitations |
|---|---|---|
| EventStoreDB | Pure event sourcing | Single-purpose |
| PostgreSQL | Existing Postgres stack | Manual implementation |
| Kafka | High-throughput streaming | Not ideal for per-stream queries |
| DynamoDB | Serverless, AWS-native | Query limitations |
| Marten | .NET ecosystems | .NET specific |
Templates
Template 1: PostgreSQL Event Store Schema
-- Events table
CREATE TABLE events (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
stream_id VARCHAR(255) NOT NULL,
stream_type VARCHAR(255) NOT NULL,
event_type VARCHAR(255) NOT NULL,
event_data JSONB NOT NULL,
metadata JSONB DEFAULT '{}',
version BIGINT NOT NULL,
global_position BIGSERIAL,
created_at TIMESTAMPTZ DEFAULT NOW(),
CONSTRAINT unique_stream_version UNIQUE (stream_id, version)
);
-- Index for stream queries
CREATE INDEX idx_events_stream_id ON events(stream_id, version);
-- Index for global subscription
CREATE INDEX idx_events_global_position ON events(global_position);
-- Index for event type queries
CREATE INDEX idx_events_event_type ON events(event_type);
-- Index for time-based queries
CREATE INDEX idx_events_created_at ON events(created_at);
-- Snapshots table
CREATE TABLE snapshots (
stream_id VARCHAR(255) PRIMARY KEY,
stream_type VARCHAR(255) NOT NULL,
snapshot_data JSONB NOT NULL,
version BIGINT NOT NULL,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Subscriptions checkpoint table
CREATE TABLE subscription_checkpoints (
subscription_id VARCHAR(255) PRIMARY KEY,
last_position BIGINT NOT NULL DEFAULT 0,
updated_at TIMESTAMPTZ DEFAULT NOW()
);
Template 2: Python Event Store Implementation
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Optional, List
from uuid import UUID, uuid4
import json
import asyncpg
@dataclass
class Event:
stream_id: str
event_type: str
data: dict
metadata: dict = field(default_factory=dict)
event_id: UUID = field(default_factory=uuid4)
version: Optional[int] = None
…
FAQ
What is 'event sourcing'?
It's a way of building systems where all state changes are saved as a sequence of events, rather than just the current state. This allows you to reconstruct the state at any point in time.
What technologies are recommended?
It depends on your use case. For systems focused on event sourcing, EventStoreDB is an option. For those already using PostgreSQL, Marten (.NET), or Kafka for high-throughput streaming are also considered.
What does 'append-only' mean?
It means that events, once recorded, cannot be changed or deleted. Only new events can be added to the sequence.
Details
Original description by the author
Design and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing event persistence patterns.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2944
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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